The context window race peaked in 2026: Llama 4 Scout claims 10M tokens, Gemini 3.1 Pro offers 2M, and GPT-5.6 delivers 1M. But raw numbers hide the real question — what does context length actually buy?
The Specs
Llama 4 Scout (17B): 10M tokens. Gemini 3.1 Pro: 2M. GPT-5.6 family: 1M. Claude Opus 5: 200K. The spread is enormous — but practical usefulness diverges from the spec sheet.
What Long Context Actually Buys
Codebase-level reasoning, book-length document analysis, and whole-company context in a single call. At 1M+ tokens you stop chunking — the model reads the entire input. That’s genuinely new capability.
The Reality Check
Long contexts are expensive (attention costs scale with input) and suffer the “lost in the middle” problem — models forget what’s in the middle of huge inputs. Memory at 10M tokens often degrades at the extremes. Effective context, not nominal context, is what matters.
Practical Guidance
Match context to workload: 200K covers most enterprise needs, 1M+ for whole-codebase work. Use RAG for very large corpora instead of brute-force context. The AI Model Comparison 2026 compares the full context landscape.
